A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.

Research highlights
► We propose a student modeling for Intelligent Virtual Environments for Training with a rich data model about a student.
► The data model, formalized as an ontology, provides ease of reuse and extension for different Intelligent Tutoring Systems.
► We present a extensive taxonomy of pedagogical diagnosis rules reusable in different learning context.
► The diagnosis method considers the interaction possibilities derived from the use of Virtual Environments.
► The conclusions reached by the diagnosis process allow sensible tutoring decisions during a learning session.